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Until now, deep learning has been widely used in the functional analysis of DNA sequences, including DeepSEA, DanQ, DeepATT and TBiNet. However, these methods have the problems of high computational complexity and not fully considering the distant interactions among chromatin features, thus affecting the prediction accuracy. In this work, we propose a hybrid deep neural network model, called DeepFormer, based on convolutional neural network (CNN) and flow-attention mechanism for DNA sequence function prediction. In DeepFormer, the CNN is used to capture the local features of DNA sequences as well as important motifs. Based on the conservation law of flow network, the flow-attention mechanism can capture more distal interactions among sequence features with linear time complexity. We compare DeepFormer with the above four kinds of classical methods using the commonly used dataset of 919 chromatin features of nearly 4.9 million noncoding DNA sequences. Experimental results show that DeepFormer significantly outperforms four kinds of methods, with an average recall rate at least 7.058% higher than other methods. Furthermore, we confirmed the effectiveness of DeepFormer in capturing functional variation using Alzheimer\u2019s disease, pathogenic mutations in alpha-thalassemia and modification in CCCTC-binding factor (CTCF) activity. We further predicted the maize chromatin accessibility of five tissues and validated the generalization of DeepFormer. The average recall rate of DeepFormer exceeds the classical methods by at least 1.54%, demonstrating strong robustness.<\/jats:p>","DOI":"10.1093\/bib\/bbad095","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T18:36:44Z","timestamp":1678819004000},"source":"Crossref","is-referenced-by-count":19,"title":["DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences"],"prefix":"10.1093","volume":"24","author":[{"given":"Zhou","family":"Yao","sequence":"first","affiliation":[{"name":"Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement, , Wuhan 430070 , China"},{"name":"Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals, , Wuhan 430070 , China"},{"name":"Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics, , Wuhan 430070 , China"},{"name":"Huazhong Agricultural University College of Informatics, , Wuhan 430070 , China"}]},{"given":"Wenjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University College of Informatics, , Wuhan 430070 , China"}]},{"given":"Peng","family":"Song","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University College of Plant Science & Technology, , Wuhan 430070 , China"}]},{"given":"Yuxue","family":"Hu","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University College of Informatics, , Wuhan 430070 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9165-4012","authenticated-orcid":false,"given":"Jianxiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement, , Wuhan 430070 , China"},{"name":"Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals, , Wuhan 430070 , China"},{"name":"Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics, , Wuhan 430070 , China"},{"name":"Huazhong Agricultural University College of Informatics, , Wuhan 430070 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"issue":"23","key":"2024041512000332600_ref1","doi-asserted-by":"crossref","first-page":"9362","DOI":"10.1073\/pnas.0903103106","article-title":"Potential etiologic and functional implications of genome-wide association loci for human diseases and traits","volume":"106","author":"Hindorff","year":"2009","journal-title":"Proc Natl Acad Sci"},{"key":"2024041512000332600_ref2","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.tibs.2014.07.002","article-title":"Absence of a simple code: how transcription factors read the genome","volume":"39","author":"Slattery","year":"2014","journal-title":"Trends Biochem 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